Interactions between man and machine as the basis of economics at every level

1/20. Your trading company is planning to implement a platform to analyze customer behavior using AI. There are questions about privacy and ethics. How do you respond to these concerns?

Explain the purpose for which the data is analyzed and the benefits of doing so. Say that since the customer clicked “I accept,” everything is fine. Collect data in accordance with regulations, but you don't tell customers how it is processed. Implement transparency: the customer knows what data is being collected, has access to the results and the ability to modify or withdraw consent.

2/20. You’re asked to evaluate how automation impacts job satisfaction. What angle do you prioritize in your analysis?

Focus only on productivity and cost reduction. Link tech use to meaning, motivation, and growth. Explore workflow changes and skill transitions. Survey staff but skip open feedback discussion.

3/20. You’re part of a project team responsible for designing a new service involving predictive technologies. How will you approach building a relationship with the user?

Consider the user's needs at the testing stage, but not during design. Design the system according to the algorithm's predictions, without the audience's input. Ignore his/her needs, because the technology “knows better” anyway. Involve users from the beginning, testing different versions and constantly modifying the design according to real reception.

4/20. As you work on a new local service model, you see the growing presence of decision-support technology. How should you include humans in the process?

Integrate people and technology in a shared responsibility model. Allow machines to act independently and humans to act as supervisors. Reduce the human presence, since algorithms work faster. Design a system in which humans complement the decisions made by machines.

5/20. Your company is implementing automation of the customer service department. You are concerned that human contact will completely disappear. What approach will be the most sustainable?

Create a model where technology supports people in real time, allowing you to maintain the relational nature of service and ensuring continuity of human contact. Automate most processes, leaving humans only for emergencies. Integrate automation with a human role that oversees quality and responds in situations that require empathy. Replace all employees with chatbots to reduce costs.

6/20. Your team is about to implement a new analytics system based on machine learning. Some people are concerned that they will not have a say in the decisions made. How do you respond?

Together with your team, build an approach where data supports dialogue and the system is the tool, not the decision-maker. Assure that decisions will always be made “somewhere higher up”. Show everyone how to interpret the data and how humans can still influence its use. Explain that the system “knows better” and is not worth questioning.

7/20. In sales department, you notice that salespeople treat CRM as a control tool rather than a support tool. How do you change their attitude toward the technology?

Encourage use of the system, but don't offer training or support. Change the way of implementation: together with the team you match the tools to their work style, show their impact on customer relations and results. Blindly ignore their resistance and enforce reporting without talking to them. Show the concrete benefits of automating tasks and saving time.

8/20. Your company is piloting robotic assistants in a warehouse. What’s your approach to ensuring they support - not disrupt - staff?

Allow collaboration but limit robot adaptability. Let robots work independently, without team contact. Design shared workflows where both roles align. Assign robots simple tasks without team coordination.

9/20. In response to the growing integration of artificial intelligence across various industries, you are tasked with developing a vocational training program centred on data analysis. The objective is to equip learners with the skills necessary to operate effectively in AI-enhanced environments, where collaboration between human expertise and machine intelligence is paramount. Recognizing the diverse backgrounds of your students and the evolving demands of the job market, how would you structure the program to best achieve this goal?

Include case studies showing AI's impact on business decisions. Focus solely on basic spreadsheet functions. Emphasize mathematical theories and coding without context. Create simulations where learners interpret AI-generated data and make informed decisions.

10/20. A smart work scheduling system is being implemented by your company. After the first weeks of operation, there are signs that some employees feel they are being treated unfairly. You are responsible for assessing the situation and making further decisions. How do you react?

Assume that the system works according to the algorithm, so there is no need to explain anything to employees. Gather feedback from employees, analyze data from the system and suggest adjustments to schedules. Involve workers in reviewing the system, organize joint tests, identify gaps and make transparent changes together with the technical team. Explain workers that mistakes are possible, but you see no reason to make changes without the approval of the IT department.

11/20. Your logistics company is in the process of rolling out an AI-powered route optimization system for drivers. While the technology promises faster deliveries and fuel savings, several experienced drivers question the accuracy of the suggestions and feel their local knowledge is being ignored. You’ve been asked to increase trust in the system while ensuring efficiency gains. How do you proceed?

Explain that it will probably get better over time and hope for adoption. Tell drivers to follow the system blindly and stop questioning it. Provide workshops comparing system recommendations with real-time outcomes. Facilitate hands-on training where drivers test and evaluate route options, give feedback, and adjust systems collaboratively.

12/20. The HR department in your company is deploying robotic process automation (RPA) to handle administrative tasks like leave approvals and performance tracking. While this should free up time for more meaningful work, many employees are worried about surveillance and losing control over their own data. You’ve been asked to introduce the system in a way that reduces fear and builds understanding. What’s your approach?

Organize workshops explaining how it works, involve staff in process design, and ensure transparent communication from day one. Hold a general briefing to explain its function and benefits. Send out a mass email announcing the change without context. Launch it quietly and hope no one notices.

13/20. A city transport agency is considering automating key functions of its public bus network, including navigation, scheduling, and dispatch operations. The goal is to improve route efficiency, reduce delays, and minimize human error. However, many bus operators and dispatch staff worry that their expertise is being undervalued - and that automation could lead to job losses. Union representatives are requesting clarity about future roles, and the public is raising concerns about safety, especially in complex urban environments. You’ve been brought in as a consultant to help shape the strategy in a way that embraces innovation while preserving human value and trust. What do you propose?

Retrain operators to oversee automated systems from centralized control rooms, focusing on technical monitoring and system reliability. Keep operators employed only for rare emergency interventions, minimizing their active involvement in daily operations. Suggest replacing all operators with automation as a cost-saving measure, assuming full trust in the system's performance. Redesign operator roles to include real-time data management, direct communication with passengers, and rapid response to unexpected events or system errors.

14/20. A manufacturing client is preparing to integrate collaborative robots (cobots) into their production line. The leadership team wants rapid adoption, but workers on the floor are unclear about how their responsibilities will change. There are concerns about safety, job security, and skill fit. You’re responsible for guiding the rollout process. How do you proceed?

Recommend limiting cobot use to a single repetitive task. Design adaptive workflows where cobots support human skills, involve staff in planning, and include shared performance goals. Install cobots immediately and let staff adjust on their own. Train workers to cooperate with cobots using basic safety routines.

15/20. Your retail company is transitioning from manual inventory checks to an advanced system utilizing smart sensors and AI algorithms. While this promises real-time stock updates and improved supply chain efficiency, many employees express concern about the reliability of the technology and fear that their roles may become redundant. As the operations manager, how do you address these concerns and facilitate a smooth transition?

Provide comprehensive training focused on interpreting and responding to the system’s outputs. Avoid discussing the new system to prevent alarming staff. Assign only tech-savvy employees to interact with the new technology. Redefine job roles to emphasize human oversight, critical decision-making, and handling exceptions that the AI system may not address, positioning employees as essential partners in the technology integration.

16/20. In response to increasing automation, your organization is revising job descriptions to reflect the evolving balance between human and machine tasks. Employees are uncertain about how their roles will change and what skills they need to develop. As the HR director, how do you proceed to ensure clarity and engagement?

Eliminate all tasks from job descriptions that could potentially be automated. Analyse current responsibilities to identify areas where machines can assist, and update descriptions accordingly. Retain existing job descriptions and simply add “plus technology use” to each. Collaborate with employees to co-create new role profiles that clearly define the synergy between human skills and technological capabilities.

17/20. A nation introduces laws to regulate AI and robotics across sectors. Effective AI regulation should aim to:

Establish a regulatory framework that encourages innovation while ensuring transparency, data protection, algorithmic accountability and protection of workers’ rights. Delegate AI governance primarily to industry leaders, assuming market forces will organically resolve ethical challenges. Suspend AI deployment until all long-term ethical consequences are scientifically proven and universally agreed upon. Introduce protectionist policies that limit foreign AI technologies to preserve national industries and employment.

18/20. Urban planners want to use AI and IoT to create a smart city. For long-term sustainability, what should be prioritized?

Integrate privacy-by-design principles, establish transparent data governance and ensure citizen participation in decision-making. Consult only technical experts, assuming civic participation would delay innovation. Centralize citizen data collection to optimize services, while ethical decisions are postponed until systems are fully deployed. Replace traditional public service roles with automated systems as a cost-saving priority, regardless of public sentiment.

19/20. You are responsible for AI governance in a public institution. How do you ensure AI systems make fair decisions for citizens?

Limit public transparency to protect intellectual property and avoid public misunderstanding of complex AI models. Implement audit mechanisms, ensure explainability of AI decisions, involve interdisciplinary ethics committees and maintain human oversight in critical cases. Suspend all AI systems at the first sign of bias, regardless of societal or economic impact. Rely on AI outputs for high-volume decisions and conduct periodic manual reviews only if public complaints arise.

20/20. You want to prepare your students or employees for the automated economy. Which approach best supports long-term employability?

Focus on teaching discipline-specific theory, assuming adaptability can be learned later in the workplace. Prioritize manual skills to preserve traditional employment sectors in case automation fails. Develop a balanced curriculum that combines AI/data literacy, critical thinking, creativity, ethical reasoning and continuous learning skills. Limit digital education to basic computer use to avoid overwhelming learners with complex technologies.

Your result: /100

You have achieved a Low Readiness Index. Your approach to human–machine collaboration remains at an early stage. You often rely on automation without fully integrating your own judgment or critical interpretation of data. Ethical safeguards, bias prevention, and user participation in design are often missing, which can lead to mistrust and reduced adoption. In several areas, you use technology as a replacement rather than as a partner to human skills, overlooking opportunities for shared responsibility models and transparent communication. To improve, you should follow these steps.

Steps to be taken to improve your Readiness Index:

Prioritise embedding human oversight in all tech-driven processes.
Create hybrid workflows where machines enhance but do not dictate decisions.
Involve diverse stakeholders in design and testing.
Take targeted training to build confidence in interpreting and challenging AI outputs.
Strengthen communication to clearly explain how decisions are made and build user trust.

You have achieved a Moderate Readiness Index. Many of your responses indicate an awareness of the value of hybrid human–machine models, ethical reviews, and user inclusion, but these are not consistently selected. In some cases, you implement automation decisions without adequate feedback loops or cross-departmental input, and you use data without sufficient critical analysis. To strengthen your readiness, you should follow the steps below.

Steps to be taken to improve your Readiness Index:

Make ethics and bias audits standard practice.
Involve diverse teams early in project design.
Ensure training goes beyond technical operation to include strategic use of technology.
Introduce scenario-based simulations where teams jointly interpret AI outputs, compare them with human insights, and adjust processes accordingly.

You have achieved a High Readiness Index. Your results indicate strong ability to combine human insight with advanced technologies in a balanced and ethical way. You show confidence in interpreting data critically, recognising when human judgment should guide or adjust automated outputs, and maintaining transparency in decision-making. You also demonstrate skill in engaging stakeholders, designing solutions with user needs in mind, and integrating ethics into everyday practice. The scenario choices suggest you adapt quickly to change, approach automation as a partner rather than a replacement, and can manage both the technical and human dimensions of Industry 5.0. This readiness level confirms you are well prepared to operate in environments where collaboration between people and intelligent systems is central. To refine your skills even further, you should follow these steps:

Continue testing advanced personalisation in intelligent systems.
Explore multi-user collaboration tools.
Share best practices to strengthen wider readiness.

EQF level alignment

According to your results, your current competence level can be estimated as %EQF%.

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Interactions between man and machine as the basis of economics at every level